The flute doesn’t make for a good example, as the end user can take it and modify it as they wish, including third party parts.

If we force it: It would be if the manufacturer made it such that all (even third party) parts for These flutes can only be distributed through their store, and they use this restriction to force any third party to comply with additional requirements.

The key problem is isn’t including third party parts, it is actively blocking the usage of third party parts, forcing additional rules (which affect existing markets, like payment processors) upon them, making use of control and market dominance to accomplish this.

The Microsoft case was, in my view, weaker than this case against Apple, but their significant market dominance in the desktop OS market made it such that it was deemed anti-competitive anyways. It probably did not help that web standards suffered greatly when MS was at the helm, and making a competive compatible browser was nigh impossible.

Why would company A need to accomodate any other “app store” in their product, especially if one of their product’s selling point is how streamlined it is?

Why should Microsoft allow for other browsers to be installed on Windows? Why should Google allow for other search engines being selectable on Android and in Chrome? The reason in all these cases is the same: it is anti-competitive, and creates a monopoly. This results in unfairly high costs to users, where these users are 3rd party software developers or end users. Due to this countries have laws against this.

Companies obviously wouldn’t want to accommodate others in ways that cost them money, but that does not make it morally acceptable from a societal point of view.

Yes, true, but that is assuming:

1. Any potential future improvement solely comes from ingesting more useful data.
2. That the amount of data produced is not ever increasing (even excluding AI slop).
3. No (new) techniques that makes it more efficient in terms of data required to train are published or engineered.
4. No (new) techniques that improve reliability are used, e.g. by specializing it for code auditing specifically.

What the author of the blogpost has shown is that it can find useful issues even now. If you apply this to a codebase, have a human categorize issues by real / fake, and train the thing to make it more likely to generate real issues and less likely to generate false positives, it could still be improved specifically for this application. That does not require nearly as much data as general improvements.

While I agree that improvements are not a given, I wouldn’t assume that it could never happen anymore. Despite these companies effectively exhausting all of the text on the internet, currently improvements are still being made left-right-and-center. If the many billions they are spending improve these models such that we have a fancy new tool for ensuring our software is more safe and secure: great! If it ends up being an endless money pit, and nothing ever comes from it, oh well. I’ll just wait-and-see which of the two will be the case.

Not quite, though. In the blogpost the pentester notes that it found a similar issue (that he overlooked) that occurred elsewhere, in the logoff handler, which the pentester noted and verified when spitting through a number of the reports it generated. Additionally, the pentester noted that the fix it supplied accounted for (and documented) a issue that it accounted for, that his own suggested fix for the issue was (still) susceptible to. This shows that it could be(come) a new tool that allows us to identify issues that are not found with techniques like fuzzing and can even be overlooked by a pentester actively searching for them, never mind a kernel programmer.

Now, these models generate a ton of false positives, which make the signal-to-noise ratio still much higher than what would be preferred. But the fact that a language model can locate and identify these issues at all, even if sporadically, is already orders of magnitude more than what I would have expected initially. I would have expected it to only hallucinate issues, not finding anything that is remotely like an actual security issue. Much like the spam the curl project is experiencing.

It is unclear to me what you are trying to accomplish, do you want to update the elements for where masked?

Generally, faster moving traffic necessitates more space between vehicles for a safe stopping distance. Often this distance is specified in seconds as to account for this speed. Road capacity is hence barely affected by changes in speed, only your travel time, if you can get on the road, is. [1]

Moreover, in a city with many intersections, the bottleneck is usually the intersections themselves, not the roads. Higher speeds just causes you to get to the next intersection faster, but may not improve the capacity of an intersection, reducing the travel time gains of a higher speed limit. [2]

To the contrary, the potential increase in travel time for cars could make alternatives to driving more attractive, reducing congestion instead. Furthermore, accidents tend to block roads, also causing congestion. Fewer accidents means less congestion.

Polars has essentially replaced Pandas for me. It is MUCH faster (in part due to lazy queries) and uses much less RAM, especially if the query can be streamed. While syntax takes a bit of getting used to at first, it allows me to specify a lot more without having to resort to apply with custom Python functions.

My biggest gripe is that the error messages are significantly less readable due to the high amount of noise: the stacktrace into the query executor does not help with locating my logic error, stringified query does not tell me where in the query things went wrong…

The key point that is being made is that it you are doing de facto copyright infringement of plagiarism by creating a copy, it shouldn’t matter whether that copy was made though copy paste, re-compressing the same image, or by using AI model. The product being the copy paste operation, the image editor or the AI model here, not the (copyrighted) image itself. You can still sell computers with copy paste (despite some attempts from large copyright holders with DRM), and you can still sell image editors.

However, unlike copy paste and the image editor, the AI model could memorize and emit training data, without the input data implying the copyrighted work. (exclude the case where the image was provided itself, or a highly detailed description describing the work was provided, as in this case it would clearly be the user that is at fault, and intending for this to happen)

At the same time, it should be noted that exact replication of training data isn’t exactly desirable in any case, and online services for image generation could include a image similarity check against training data, and many probably do this already.

Republicans however also: deport people with a legal right to be in the country, including citizens, without due process. Want to destroy all progress made on issues affecting the LGBTQ+ community. Wish to reduce women’s rights, some including voting rights. Want to abolish the separation between church and state.

Even if there is a close resemblance between the two parties on Gaza, but there are plenty of other issues where they are still incomparable, and ignoring these differences and calling both parties equally bad does not help.

At least the AI runs locally, as opposed to sending everything to someone else’s computer for processing. Local translation in Firefox actually works quite well.